Watch this video to gain an impression about GUSAR’s look-and-feel:
Enhanced choice of variables:
Improved algorithm of model building and prediction
Automatic, simplified creation of QSAR models
Optimized handling of very large data sets
Download your free trial version of GUSAR:
Please, download and read first the installation instructions (strongly recommended):
How to install GUSAR (pdf, 300 kB).
GUSAR runs under the operating systems Windows® XP / Vista / 7. For the program to install and run, you need about 100 MB free disc space, and at least 1 GB RAM (2 GB or more are recommended).
If all this fits, please download the following zip archive to your local machine (it is a big file, so it may take some time):
GUSAR trial version (zip, 29 MB).
Please, be aware that this version provides you with the full functionality of GUSAR, but will automatically expire after a trial period of 1 week, starting with the installation of the program. Extending the usage then requires a license key, which we will gladly provide when you contact us under info(at)genexplain.com.
In case you have any questions after reading the instructions, or you encounter any problems during the installation, don’t hesitate to contact us.
Academic one seat 1-year license
Commercial one seat 1-year license
Free trial version
1000 €
2000 €
0 €
Vijay H. Masand et al., General Unrestricted Structure Activity Relationships based evaluation of quinoxaline derivatives as potential influenza NS1A protein inhibitors, Der Pharma Chemica 3(4):517-525 (2011).
Ozturk I.I., Yarar S., Banti C.N., Kourkoumelis N., Chrysouli M.P., Manoli M., Tasiopoulos A.J., Hadjikakou S.K. (2017). QSAR studies on antimony(III) halide complexes with N-substituted thiourea derivatives, Polyhedron, 123, 152-161. Link.
Zakharov A.V., Varlamova E.V., Lagunin A.A., Dmitriev A.V., Muratov E.N., Fourches D., Kuz’min V.E., Poroikov V.V., Tropsha A., Nicklaus M.C. (2016). QSAR Modeling and Prediction of Drug-Drug Interactions. Molecular Pharmaceutics, 13 (2), 545–556. Link.
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Mansouri K., Abdelaziz A., Rybacka A. et al. (2016). CERAPP: Collaborative Estrogen Receptor Activity Prediction Project. Environmental Health Perspectives, 124 (7), 1023-1033. Full text.
Hadjikakou S.K., Ozturka I.I., Banti C.N., Kourkoumelis N., Hadjiliadis N. (2015). Recent advances on antimony(III/V) compounds with potential activity against tumor cells. Journal of Inorganic Biochemistry, 153, 293–305. Link.
Khayrullina V.R., Gerchikov A.Ya., Lagunin A.A., Zarudii F.S. (2015). Quantitative analysis of structure-activity relationships of tetrahydro-2H-isoindole cyclooxygenase-2 inhibitors. Biochemistry Moscow, 80 (1), 74-86. Link.
Fedorova, E.V., Buryakina, A.V., Zakharov, A.V., Filimonov, D.A., Lagunin, A.A., Poroikov, V.V. (2014) Design, synthesis and pharmacological evaluation of novel vanadium-containing complexes as antidiabetic agents. PLoS One
Lagunin A.A., Gloriozova T.A., Dmitriev A.V., Volgina N.E., Poroikov V.B. (2013). Computer Evaluation of Drug Interactions with P-Glycoprotein. Bull. Exp. Biol. Med., 154 (4), 521-524. Link.
Mahajan D.T., Masand V.H., Patil K.N., Hadda T.B., Rastija V. (2013). Integrating GUSAR and QSAR analyses for antimalarial activity of synthetic prodiginines against multi drug resistant strain. Med. Chem. Res., 22 (5), 2284–2292. Link.
Zakharov, A.V., Lagunin, A.A., Filimonov, D.A., Poroikov, V.V. (2012) Quantitative prediction of antitarget interaction profiles for chemical compounds. Chem. Res. Toxicol.
Zakharov, A.V., Peach, M.L., Sitzmann, M., Filippov, I.V., McCartney, H.J., Smith, L.H., Pugliese, A., Nicklaus, M.C. (2012) Computational tools and resources for metabolism-related property predictions. 2. Application to prediction of half-life time in human liver microsomes. Future Med. Chem.
Kokurkina G.V., Dutov M.D., Shevelev S.A., Popkov S.V., Zakharov A.V., Poroikov V.V. (2011) Synthesis, antifungal activity and QSAR study of 2-arylhydroxynitroindoles. Eur. J. Med. Chem.
Lagunin A., Zakharov A., Filimonov D., Poroikov V. (2011) QSAR modelling of rat acute toxicity on the basis of PASS prediction. Mol. Inform.
Filimonov D.A., Zakharov A.V., Lagunin A.A., Poroikov V.V. (2009) QNA based ‘Star Track’ QSAR approach. SAR QSAR Environ. Res.
Lagunin A., Zakharov A., Filimonov D., Poroikov V. (2009). In silico assessment of acute toxicity in rodents. Toxicol. Lett.
Filimonov D.A., Poroikov V.V. (2008) Probabilistic approach in activity prediction. In: Chemoinformatics Approaches to Virtual Screening. Eds. Alexandre Varnek and Alexander Tropsha. Cambridge (UK): RSC Publishing, pp. 182-216.
Filimonov D.A., Poroikov V.V. (2006) Prediction of biological activity spectrum for organic compounds. Russ. Chem. J.
Filimonov D.A., Lagunin A.A., Poroikov V.V. (2005) Prediction of activity spectra for substances using new local integrative descriptors. QSAR and Molecular Modelling in Rational Design of Bioactive Molecules. Eds. Esin Aki Sener, Ismail Yalcin, Ankara (Turkey), CADD & D Society, pp. 98-99.
Poroikov V. V., Filimonov D. A., Borodina Yu. V., Lagunin A. A., Kos A. (2000) Robustness of Biological Activity Spectra Predicting by Computer Program PASS for Noncongeneric Sets of Chemical Compounds. J. Chem. Inf. Comput. Sci.
Filimonov D., Poroikov V., Borodina Yu., Gloriozova T. (1999) Chemical Similarity Assessment through Multilevel Neighborhoods of Atoms: Definition and Comparison with the Other Descriptors. J. Chem. Inf. Comput. Sci.
Poroikov V. V., Filimonov D. A., Borodina Yu. V., Gloriozova T. A., Sitnikov V. B., Sadovnikov S. V., Sosnov A. V. (2004) Quantitative relationships between structure and delayed neurotoxicity of chemicals studied by the Self-Consistent Regression method using the PASS program. Pharmaceutical Chemistry Journal
Filimonov D. A., Akimov D. V., Poroikov V. V. (2004) The method of Self-Consistent Regression for the quantitative analysis of relationships between structure and properties of chemicals. Pharmaceutical Chemistry Journal